UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.
arXiv preprint arXiv:2305.05227 , year=
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Uncertainty-based Debiasing and Unlearning for Decontamination
UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.